Epigenetic age acceleration mediates the association between smoking and diabetes-related outcomes

Chang, Xiao; Lin, Wan-Yu

doi:10.1186/s13148-023-01512-x

Cited by 3 publications

(3 citation statements)

References 51 publications

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“…[36][37][38] Consequently, smoking may lead to SVS by accelerating PhenoAge and could contribute to other diseases via the same mechanism, thereby indirectly increasing the risk of SVS. 39 In conclusion, smoking signi cantly increases the risk of SVS, partly through the mediation of epigenetic age. Although the risk factors associated with initial smoking are unmodi able, other adjustable lifestyle factors can potentially decelerate epigenetic age, thereby reducing the risk of SVS.…”

Section: Discussionmentioning

confidence: 93%

Causal Associations of Epigenetic Age Acceleration with Stroke and Its Functional outcome: A Two-Sample, Two-Step Mendelian Randomization Study

Qiu,

Wen,

et al. 2024

Preprint

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Background Emerging evidence from observational studies suggested that epigenetic age acceleration may result in an increased incidence of stroke and poorer functional outcomes after a stroke. However, the causality of these associations remains controversial and may be confounded by bias. We aimed to investigate the causal effects of epigenetic age on stroke and its functional outcomes. Methods We conducted a two-sample Mendelian randomization (MR) analysis to explore the causal relationships between epigenetic age and stroke and its outcomes. Additionally, a two-step MR analysis was performed to investigate whether lifestyle factors affect stroke via epigenetic age. Datasets of epigenetic age were obtained from a recent meta-analysis (n = 34710), while those of stroke and its outcomes were sourced from the MEGASTROKE (n = 520000) consortium and Genetics of Ischaemic Stroke Functional Outcome network (n = 6165). Results Two-sample MR analysis revealed a causal relationship between PhenoAge and small vessel stroke (OR = 1.07; 95% CI, 1.03–1.12; p = 2.01 × 10− 3). Mediation analysis through two-step MR indicated that the increased risk of small vessel stroke due to smoking initiation was partially mediated by PhenoAge, with a mediation proportion of 9.5% (95% CI, 1.6–20.6%). No causal relationships were identified between epigenetic age and stroke outcomes. Conclusions Our study confirms a causal relationship between epigenetic age acceleration and stroke, indicating that epigenetic age acceleration may mediate the increased stroke risk due to smoking. Interventions specifically aimed at decelerating epigenetic aging, such as specific lifestyle changes, offer effective strategies for reducing stroke risk.

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Section: Discussionmentioning

confidence: 93%

Causal Associations of Epigenetic Age Acceleration with Stroke and Its Functional outcome: A Two-Sample, Two-Step Mendelian Randomization Study

Qiu,

Wen,

et al. 2024

Preprint

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“…Regarding common risk factors, substantial evidence indicates that smoking is a causative factor of COPD. Additionally, smoking also plays a role in the progression of T2D ( 36 ). Individuals diagnosed with T2D were more likely to receive smoking cessation advice from healthcare professionals than those without T2D.…”

Section: Discussionmentioning

confidence: 99%

Causal relationship between type 2 diabetes and common respiratory system diseases: a two-sample Mendelian randomization analysis

Chen,

Zhang,

Sun

2024

Front. Med.

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BackgroundType 2 diabetes (T2D) frequently co-occurs with respiratory system diseases such as chronic obstructive pulmonary disease (COPD), bronchial asthma, lung cancer, interstitial lung disease, and pulmonary tuberculosis. Although a potential association is noted between these conditions, the available research is limited.ObjectiveTo investigate the causal relationship between patients with T2D and respiratory system diseases using two-sample Mendelian randomization analysis.MethodsCausal relationships were inferred using a two-sample Mendelian randomization (MR) analysis based on publicly available genome-wide association studies. We employed the variance inverse-weighted method as the primary analytical approach based on three key assumptions underlying MR analysis. To bolster the robustness and reliability of our results, we utilized MR Egger’s intercept test to detect potential pleiotropy, Cochran’s Q test to assess heterogeneity, funnel plots to visualize potential bias, and “leave-one-out” sensitivity analysis to ensure that our findings were not unduly influenced by any single genetic variant.ResultThe inverse variance weighted (IVW) analysis indicated a causal relationship between T2D and COPD [Odds Ratio (OR) = 0.87; 95% Confidence Interval (CI) = 0.82–0.96; p < 0.05]. No significant heterogeneity or pleiotropy were observed through their respective tests (p > 0.05), and the statistical power calculations indicated that the results were reliable. The IVW analysis showed a negative causal relationship between T2D and bronchial asthma [OR = 0.85; 95% CI = 0.81–0.89; p < 0.05]. However, the IVW under the random-effects model indicated heterogeneity (p < 0.05), suggesting instability in the results and requiring cautious interpretation. The study found a positive causal relationship between T2D and pulmonary tuberculosis (OR = 1.24, 95% CI = 1.05–1.45, p < 0.05). However, they exhibited pleiotropy (p < 0.05), indicating their instability. No correlation between T2D and interstitial lung disease or lung cancer was observed.ConclusionT2D is negatively associated with COPD, suggesting that T2D may reduce the risk of developing COPD. A negative causal relationship between T2D and bronchial asthma has been observed, but the results exhibit heterogeneity. There is a positive causal relationship between T2D and pulmonary tuberculosis, yet the findings suggest the presence of pleiotropy. No significant causal relationship between T2D and lung cancer or interstitial lung disease was observed.

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“…However, most epigenetic clocks have been constructed using DNAm signatures of blood-based tissues such as whole blood (i.e., leukocytes) or peripheral blood mononuclear cells (PBMCs) (Hannum et al 2013;Levine et al 2018;Lu et al 2022;Belsky et al 2022). Given the challenges in blood collection for large population-based cohorts, there is a growing desire among researchers to use less invasive tissues, such as buccal or saliva (Raffington et al 2021;Kim, Joyce, et al 2023;Kim, Yaffe, et al 2023;Milicic et al;Chang & Lin 2023;Loh et al 2023). These tissues do not require trained phlebotomists, can be collected in home settings, and there is less hesitancy among study participants to provide oral-based samples in comparison to having their blood drawn.…”

Section: Introductionmentioning

confidence: 99%

Cross-Tissue Comparison of Epigenetic Aging Clocks in Humans

Apsley,

Ye,

Caspi

et al. 2024

Preprint

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Epigenetic clocks are a common group of tools used to measure biological aging – the progressive deterioration of cells, tissues and organs. Epigenetic clocks have been trained almost exclusively using blood-based tissues but there is growing interest in estimating epigenetic age using less-invasive oral-based tissues (i.e., buccal or saliva) in both research and commercial settings. However, differentiated cell types across body tissues exhibit unique DNA methylation landscapes and age-related alterations to the DNA methylome. Applying epigenetic clocks derived from blood-based tissues to estimate epigenetic age of oral-based tissues may introduce biases. We tested the within-person comparability of common epigenetic clocks across five tissue types: buccal epithelial, saliva, dry blood spots, buffy coat (i.e., leukocytes), and peripheral blood mononuclear cells. We tested 284 distinct tissue samples from 83 individuals aged 9-70 years. Overall, there were significant within-person differences in epigenetic clock estimates from oral-based versus blood-based tissues, with average differences of almost 30 years observed in some age clocks. In addition, most epigenetic clock estimates of blood-based tissues exhibited low correlation with estimates from oral-based tissues despite controlling for cellular proportions and other technical factors. Our findings indicate that application of blood-derived epigenetic clocks in oral-based tissues may not yield comparable estimates of epigenetic age, highlighting the need for careful consideration of tissue type when estimating epigenetic age.

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Epigenetic age acceleration mediates the association between smoking and diabetes-related outcomes

Cited by 3 publications

References 51 publications

Causal Associations of Epigenetic Age Acceleration with Stroke and Its Functional outcome: A Two-Sample, Two-Step Mendelian Randomization Study

Causal Associations of Epigenetic Age Acceleration with Stroke and Its Functional outcome: A Two-Sample, Two-Step Mendelian Randomization Study

Causal relationship between type 2 diabetes and common respiratory system diseases: a two-sample Mendelian randomization analysis

Cross-Tissue Comparison of Epigenetic Aging Clocks in Humans

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